Biomedical image processing has experienced dramatic expansion, and has been an interdisciplinary research field attracting expertise from applied mathematics, computer sciences, engineering, statistics, physics, biology and medicine. Computer-aided diagnostic processing has already become an important part of clinical routine. Accompanied by a rush of new development of high technology and use...

The paper deals with the problem of converting medical images from one format to another. In solving it the structure of the most commonly used medical image formats are studied and analysed. A mechanism for medical image file conversion and a Medical Internal Image Format (MIIF) is proposed as a solution. A medical image conversion system (MICS), designed and implemented to perform conversions...

By image analysis we mean the operation of extracting a relativeiy small amount of quantitative information from a two-dimensional image. Typically, a two-dimensional image, in digital form, consists of an array of 512 x 512 eight-bit digital words. The quantitative information to be extracted consists of a few thousand digital words. These words may describe, for example, the contours of cell ...

Medical image analysis is performed in order to facilitate medical research and ultimately provide better healthcare. It is critical to the advancement of imaging-based medical research, for example, using magnetic resonance (MR) imaging to probe brain structural and functional changes related to a disease or cognitive process. This special issue highlights new methods of signal and image proce...

Image data is of immense practical importance in medical informatics. Medical images, such as Computed Axial Tomography (CAT), Magnetic Resonance Imaging (MRI), Ultrasound, and X-Ray, in standard DICOM formats are often stored in Picture Archiving and Communication Systems (PACS) and linked with other clinical information in EHR clinical management systems. Research efforts have been devoted to...

We propose a novel approach to landmark-based medical image registration based on the geostatical method of Kriging prediction. Our method exploits the spatial statistical relation between two images, as estimated using generalpurpose registration algorithms, in order to construct an optimum predictor of the displacement field. High accuracy is achieved by using an estimated spatial model of th...